A combined finite element and hierarchical Deep learning approach for structural health monitoring: Test on a pin-joint composite truss structure

Abstract

Structural Health Monitoring (SHM) is an emerging field of engineering with a wide range of applications. The most common SHM strategies operate on structural responses through vibration measurements and focus on training mathematical classifiers which are used after to identify damage in unknown responses. Classifiers may additionally locate damage when adequate labeled damaged data is available. In the present work, a novel SHM method is presented where labeled damaged data is generated through FE models for a pin-joint composite truss structure employing a model-based approach for the problem of data acquisition. The truss is made of carbon fiber reinforced polymer (CFRP) members joint on aluminum connections forming a complex and large FE problem. A Deep Learning (DL) Convolutional Neural Network (CNN) classifier is trained on the FE generated vibration data combined with a hierarchical multiple damage identification and location scheme. The numerically trained CNN is after validated on experimental statuses of the truss in both damage detection and location, proving to be robust and accurate for the considered test case. The potential of hierarchical CNNs with FE based SHM data for multiple damages is investigated in this work and a comparison is given between hierarchical and direct multiclass CNNs. The large performance gains of the former are proven for the studied experimental case highlighting also the importance of SHM system architectures with CNNs.